STEM student retention can be enhanced through mentorship & research experiences.

Jennifer Wang, Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology
Matthew Yeh, Paulson School of Engineering & Applied Sciences, Harvard University
James Newland, Texas Advanced Computing Center, University of Texas at Austin

Introduction

Effective mentorship programs that provide authentic research experiences can enhance retention of STEM students. Quantum Engineering Research and You (QuERY) is a collaboration between grad students and science teachers to mentor high school student researchers. QuERY uses mentorship to equip students with the skills necessary for research in quantum science, engineering, and computing. Throughout the semester, students work in small groups with their graduate student mentors via videoconferencing to develop a genuine research project. Culminates in an end-of-program research symposium, where mentors and students meet in person to present their work. Projects can be hands-on, coding or simulations, or theoretical content. Quantum science & engineering generates interest and enthusiasm among student researchers.

QuERY: Research & Mentoring

• Hybrid format: 40-60 high school students and 3 science teachers in person; 10 graduate student mentors virtual.
• Goal: science communication and excitement about science, as well as in-depth mentoring.

Program Structure/Timeline

• Mentor orientation & Kickoff (1 week): mentoring tips & protection policies
• Fundamentals of QM (9 weeks): tutorials + small group discussion: focused on building comfort with topics & getting to know the mentors, low barrier for students
• Project period (5 weeks): How to make a scientific poster:
• Sorted by research interests
• Groups choose the format of the project (journal club, science demo, or simulation)
• Groups present to their peers on last day with printed posters

Virtual Tutorials on Quantum

• Tutorials were a mix of physics content and the personal journey of the mentor scientist.
• Tutorial topics:
• Waves & Wave Mechanics
• Superposition & Entanglement
• Small group discussion topics:
• Quantization
• Quantum Logic

Hands-on Authentic Research Experiences

• Students create research products & demos
• Posters foster presentation skills

Poster Samples

Demographics

Feedback: Self-Efficacy

• > 70% of students reported gains on:
• Ability to contribute to science
• Discussing scientific concepts with others
• Working collaboratively with others
• Ability to do well in future science courses

Feedback: Mentorship

• > 80% of students reported positively on:
• Working relationship with mentor
• Time spent with mentor
• Career and school advice from mentor

Free Responses & Mentorship Continuity

• Students greatly appreciated the more interpersonal sections of talks highlighting mentors’ individual research/science journeys; key to building trust and more authentic interactions
  • From 2023, 100% of students indicated “Yes” or “Maybe” when asked if they would contact their mentor in the future for help or advice, and all students would recommend QuERY to their peers.
• Students who have remained in contact with their mentor after completion of QuERY have experienced additional gains
  • “I met wonderful people and found my passion for quantum computing. I want to pursue it in the future.”

Feedback: Motivation

• From 2024, over 50% of students wanted to participate in QuERY in order to:
  • Explore interest in science
  • Gain hands-on experience in research
  • Clarify if they wanted to pursue a scientific research career
  • Have a good intellectual challenge
  • Participate in a program with a strong reputation
  • Enhance my resume

Program details: Cost Breakdown

• Over 15 hybrid sessions, plus in-person final poster session
  • Student food: $3000
  • Mentor food/stipends: $1500
  • Transportation and lodging: $7500
  • Demo costs: $3000
• -> $15,000 in total,  including 18% overhead

Demographics: Ethnicity

Ethnicity demographics are largely aligned with national averages for computer science [1]; we will pursue more targeted recruitment strategies in the future for better representation.
[1] U.S. Bureau of Labor Statistics, “Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity,” Labor Force Statistics from the Current Population Survey, Table 11, 2022.

Acknowledgments

This work is supported by the Harvard Quantum Initiative and the MIT Center for Quantum Engineering through the Interdisciplinary Quantum Information and Engineering (iQuiSE) program. M.Y. acknowledges support from the Department of Defense through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program. J.W. acknowledges support from the National Science Foundation through the Graduate Research Fellowship Program (GRFP) under Award No. DGE174503. We thank Evelyn L. Hu, Nishant Sule, Salma Abu Ayyash, and Susan Johnson for fruitful discussions.

Enhancing CT Attitudes in Physics Students by Code-based Model-building

Visit the project page for lots more information, including code and references.

Impacting Student Attitudes About CS Through Coding Integration into Science

In this session, participants will learn how integrating computer science (CS) pedagogy into science and math classes can positively impact students’ attitudes about computer science. When students experience lessons in science and math classes that incorporate computer science (CS), they feel more confident about CS. They are more likely to pursue academic pathways that include CS. Participants will see examples of specific lessons that can be used in science and math courses to improve students’ attitudes about the use of computer science (CS) in non-CS STEM courses.

Participants will use STEMcoding during the session and can access STEMcoding for free for one year.

If you are interested in more academic details about the STEMcoding study, click here.

Improving Student Computational Thinking Outcome Expectancy through Integrating Model-building in a Secondary Physics Course

The role of computation in physics is increasing in both a professional and educational context. High school physics pedagogy should incorporate computational thinking, data science, and computer science skills to bolster learning across these domains. The study described here shows that when students build physically meaningful models using computer programming in a physics course, their outcome expectancy is positively impacted. Outcome expectancy can predict students’ future academic and professional choices. The STEMcoding platform asks students to construct models using Euler-Cromer step-wise modeling of physical phenomena, such as those described by the laws of motion. The study presented had students use STEMcoding during the kinematics portion of Advanced Placement Physics 1 classes at a large, urban high school in southeast Texas. A moderate positive impact on outcome expectancy was found across various groups in the study population. Results show how computational essays can provide scaffolded learning while allowing students to use code to create interactive models, allowing knowledge construction in a domain-specific manner.

Data Science Pedagogy can Transform how Astronomy Students Construct Knowledge.

If you are attending the Data Science Education K12 Conference in San Antonio, talk to me about data science education in astronomy classes during the individual showcases. Be sure to visit the website for my poster!

Modern astronomy research requires computation and data science tools, which are not traditionally part of the Astronomy 101 course. This computational essay describes what a spectral energy distribution (SED) is and uses code to construct one. Although existing computational thinking tools like spreadsheets can be leveraged to allow computational thinking to construct science knowledge, the needs of a student or teacher astronomical researcher will likely go beyond introductory tools. The goal of the SED computational essay presented is not only to inform about SEDs and their use but also to be a part of a toolset meant to allow students and teachers to interrogate other stellar photometric datasets at scale. This notebook was designed by alumni of the NASA/IPAC Teacher Archive Research Project (NITARP). The code can be found on GitHub. This presentation is in the iPoster format, as seen at this link.

More details about this project are available.

https://tinyurl.com/CASTPivotData

Eric Friberg and Jimmy Newland share how data science can help drive science learning through analyzing real-world phenomena.

Visit the resources page for lots of content, including slides from the training.

Astronomy datasets can be hard to use for high school astronomy classes. Data science education pedagogy can be leveraged to create astronomy activities in which students interrogate data, create visuals, and use statistical thinking to construct astronomy knowledge. This session describes how the NASA/IPAC Infrared Science Archive (IRSA) can provide a web-based interface for students to use basic data science techniques in astronomy to build data literacy while learning astronomical concepts. The activities shared will be available for anyone but were designed to be used in astro 101 classes in high school or early college.